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You searched for subject:(supervised learning). Showing records 1 – 30 of 652 total matches.

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1. Lease, Matthew A. Beyond keywords: finding information more accurately and easily using natural language.

Degree: PhD, Computer Science, 2009, Brown University

 Information retrieval (IR) has become a ubiquitous technology for quickly and easily finding information on a given topic amidst the wealth of digital content available… (more)

Subjects/Keywords: supervised learning

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APA (6th Edition):

Lease, M. A. (2009). Beyond keywords: finding information more accurately and easily using natural language. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:206/

Chicago Manual of Style (16th Edition):

Lease, Matthew A. “Beyond keywords: finding information more accurately and easily using natural language.” 2009. Doctoral Dissertation, Brown University. Accessed November 20, 2019. https://repository.library.brown.edu/studio/item/bdr:206/.

MLA Handbook (7th Edition):

Lease, Matthew A. “Beyond keywords: finding information more accurately and easily using natural language.” 2009. Web. 20 Nov 2019.

Vancouver:

Lease MA. Beyond keywords: finding information more accurately and easily using natural language. [Internet] [Doctoral dissertation]. Brown University; 2009. [cited 2019 Nov 20]. Available from: https://repository.library.brown.edu/studio/item/bdr:206/.

Council of Science Editors:

Lease MA. Beyond keywords: finding information more accurately and easily using natural language. [Doctoral Dissertation]. Brown University; 2009. Available from: https://repository.library.brown.edu/studio/item/bdr:206/


University of Illinois – Chicago

2. Mohammadi, Neshat. Supervised Tensor Learning with Applications.

Degree: 2017, University of Illinois – Chicago

 In this thesis, a new supervised tensor learning (STL) approach with application to neuroimages has been studied and implemented. We applied our proposed polynomial kernel-based… (more)

Subjects/Keywords: Supervised Tensor Learning

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APA (6th Edition):

Mohammadi, N. (2017). Supervised Tensor Learning with Applications. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/22099

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Mohammadi, Neshat. “Supervised Tensor Learning with Applications.” 2017. Thesis, University of Illinois – Chicago. Accessed November 20, 2019. http://hdl.handle.net/10027/22099.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Mohammadi, Neshat. “Supervised Tensor Learning with Applications.” 2017. Web. 20 Nov 2019.

Vancouver:

Mohammadi N. Supervised Tensor Learning with Applications. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2019 Nov 20]. Available from: http://hdl.handle.net/10027/22099.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mohammadi N. Supervised Tensor Learning with Applications. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/22099

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Oregon State University

3. Hao, Guohua. Revisiting output coding for sequential supervised learning.

Degree: MS, Computer Science, 2009, Oregon State University

 Markov models are commonly used for joint inference of label sequences. Unfortunately, inference scales quadratically in the number of labels, which is problematic for training… (more)

Subjects/Keywords: ECOC; Supervised learning (Machine learning)

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APA (6th Edition):

Hao, G. (2009). Revisiting output coding for sequential supervised learning. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/10897

Chicago Manual of Style (16th Edition):

Hao, Guohua. “Revisiting output coding for sequential supervised learning.” 2009. Masters Thesis, Oregon State University. Accessed November 20, 2019. http://hdl.handle.net/1957/10897.

MLA Handbook (7th Edition):

Hao, Guohua. “Revisiting output coding for sequential supervised learning.” 2009. Web. 20 Nov 2019.

Vancouver:

Hao G. Revisiting output coding for sequential supervised learning. [Internet] [Masters thesis]. Oregon State University; 2009. [cited 2019 Nov 20]. Available from: http://hdl.handle.net/1957/10897.

Council of Science Editors:

Hao G. Revisiting output coding for sequential supervised learning. [Masters Thesis]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/10897


Rutgers University

4. Gazzola, Gianluca. Supervised learning methods for variable importance and regression with uncertainty on dependent data.

Degree: PhD, Operations Research, 2019, Rutgers University

This dissertation covers a collection of supervised learning methods targeted to data with complex dependence patterns. Part of our work orbits around the concept of… (more)

Subjects/Keywords: Supervised learning (Machine learning)

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APA (6th Edition):

Gazzola, G. (2019). Supervised learning methods for variable importance and regression with uncertainty on dependent data. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/60158/

Chicago Manual of Style (16th Edition):

Gazzola, Gianluca. “Supervised learning methods for variable importance and regression with uncertainty on dependent data.” 2019. Doctoral Dissertation, Rutgers University. Accessed November 20, 2019. https://rucore.libraries.rutgers.edu/rutgers-lib/60158/.

MLA Handbook (7th Edition):

Gazzola, Gianluca. “Supervised learning methods for variable importance and regression with uncertainty on dependent data.” 2019. Web. 20 Nov 2019.

Vancouver:

Gazzola G. Supervised learning methods for variable importance and regression with uncertainty on dependent data. [Internet] [Doctoral dissertation]. Rutgers University; 2019. [cited 2019 Nov 20]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60158/.

Council of Science Editors:

Gazzola G. Supervised learning methods for variable importance and regression with uncertainty on dependent data. [Doctoral Dissertation]. Rutgers University; 2019. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60158/


Delft University of Technology

5. Van Hecke, K.G. Persistent self-supervised learning principle: Study and demonstration on flying robots:.

Degree: 2015, Delft University of Technology

 We introduce, study and demonstrate Persistent Self-Supervised Learning (PSSL), a machine learning method for usage onboard robotic platforms. The PSSL model leverages a standard supervised(more)

Subjects/Keywords: persistent self-supervised learning; MAV

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APA (6th Edition):

Van Hecke, K. G. (2015). Persistent self-supervised learning principle: Study and demonstration on flying robots:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd

Chicago Manual of Style (16th Edition):

Van Hecke, K G. “Persistent self-supervised learning principle: Study and demonstration on flying robots:.” 2015. Masters Thesis, Delft University of Technology. Accessed November 20, 2019. http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd.

MLA Handbook (7th Edition):

Van Hecke, K G. “Persistent self-supervised learning principle: Study and demonstration on flying robots:.” 2015. Web. 20 Nov 2019.

Vancouver:

Van Hecke KG. Persistent self-supervised learning principle: Study and demonstration on flying robots:. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2019 Nov 20]. Available from: http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd.

Council of Science Editors:

Van Hecke KG. Persistent self-supervised learning principle: Study and demonstration on flying robots:. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd


Texas A&M University

6. Perez, David Matthew. Exploring Key Variables In Wind Turbine Power Curve Modeling.

Degree: MS, Industrial Engineering, 2018, Texas A&M University

 Though substantial evidence has shown the importance of wind speed and direction in modelling a wind turbine’s power curve, there remains uncertainty as to whether… (more)

Subjects/Keywords: supervised learning; wind energy

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APA (6th Edition):

Perez, D. M. (2018). Exploring Key Variables In Wind Turbine Power Curve Modeling. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/173925

Chicago Manual of Style (16th Edition):

Perez, David Matthew. “Exploring Key Variables In Wind Turbine Power Curve Modeling.” 2018. Masters Thesis, Texas A&M University. Accessed November 20, 2019. http://hdl.handle.net/1969.1/173925.

MLA Handbook (7th Edition):

Perez, David Matthew. “Exploring Key Variables In Wind Turbine Power Curve Modeling.” 2018. Web. 20 Nov 2019.

Vancouver:

Perez DM. Exploring Key Variables In Wind Turbine Power Curve Modeling. [Internet] [Masters thesis]. Texas A&M University; 2018. [cited 2019 Nov 20]. Available from: http://hdl.handle.net/1969.1/173925.

Council of Science Editors:

Perez DM. Exploring Key Variables In Wind Turbine Power Curve Modeling. [Masters Thesis]. Texas A&M University; 2018. Available from: http://hdl.handle.net/1969.1/173925


Colorado School of Mines

7. Jackson, Ryan Blake. Machine learning for encrypted Amazon Echo traffic classification.

Degree: MS(M.S.), Computer Science, 2018, Colorado School of Mines

 As smart speakers like the Amazon Echo become more popular, they have given rise to rampant concerns regarding user privacy. This work investigates machine learning(more)

Subjects/Keywords: Supervised classification; Machine learning

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APA (6th Edition):

Jackson, R. B. (2018). Machine learning for encrypted Amazon Echo traffic classification. (Masters Thesis). Colorado School of Mines. Retrieved from http://hdl.handle.net/11124/172223

Chicago Manual of Style (16th Edition):

Jackson, Ryan Blake. “Machine learning for encrypted Amazon Echo traffic classification.” 2018. Masters Thesis, Colorado School of Mines. Accessed November 20, 2019. http://hdl.handle.net/11124/172223.

MLA Handbook (7th Edition):

Jackson, Ryan Blake. “Machine learning for encrypted Amazon Echo traffic classification.” 2018. Web. 20 Nov 2019.

Vancouver:

Jackson RB. Machine learning for encrypted Amazon Echo traffic classification. [Internet] [Masters thesis]. Colorado School of Mines; 2018. [cited 2019 Nov 20]. Available from: http://hdl.handle.net/11124/172223.

Council of Science Editors:

Jackson RB. Machine learning for encrypted Amazon Echo traffic classification. [Masters Thesis]. Colorado School of Mines; 2018. Available from: http://hdl.handle.net/11124/172223


University of Adelaide

8. Shen, Tong. Context Learning and Weakly Supervised Learning for Semantic Segmentation.

Degree: 2018, University of Adelaide

 This thesis focuses on one of the fundamental problems in computer vision, semantic segmentation, whose task is to predict a semantic label for each pixel… (more)

Subjects/Keywords: weakly supervised learning; semantic segmentation

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APA (6th Edition):

Shen, T. (2018). Context Learning and Weakly Supervised Learning for Semantic Segmentation. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/120354

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Shen, Tong. “Context Learning and Weakly Supervised Learning for Semantic Segmentation.” 2018. Thesis, University of Adelaide. Accessed November 20, 2019. http://hdl.handle.net/2440/120354.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Shen, Tong. “Context Learning and Weakly Supervised Learning for Semantic Segmentation.” 2018. Web. 20 Nov 2019.

Vancouver:

Shen T. Context Learning and Weakly Supervised Learning for Semantic Segmentation. [Internet] [Thesis]. University of Adelaide; 2018. [cited 2019 Nov 20]. Available from: http://hdl.handle.net/2440/120354.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Shen T. Context Learning and Weakly Supervised Learning for Semantic Segmentation. [Thesis]. University of Adelaide; 2018. Available from: http://hdl.handle.net/2440/120354

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Sydney

9. He, Fengxiang. Instance-Dependent Positive-Unlabelled Learning .

Degree: 2018, University of Sydney

 An emerging topic in machine learning is how to learn classifiers from datasets containing only positive and unlabelled examples (PU learning). This problem has significant… (more)

Subjects/Keywords: Postive-unlabelled learning; weakly supervised learning

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APA (6th Edition):

He, F. (2018). Instance-Dependent Positive-Unlabelled Learning . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/20115

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

He, Fengxiang. “Instance-Dependent Positive-Unlabelled Learning .” 2018. Thesis, University of Sydney. Accessed November 20, 2019. http://hdl.handle.net/2123/20115.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

He, Fengxiang. “Instance-Dependent Positive-Unlabelled Learning .” 2018. Web. 20 Nov 2019.

Vancouver:

He F. Instance-Dependent Positive-Unlabelled Learning . [Internet] [Thesis]. University of Sydney; 2018. [cited 2019 Nov 20]. Available from: http://hdl.handle.net/2123/20115.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

He F. Instance-Dependent Positive-Unlabelled Learning . [Thesis]. University of Sydney; 2018. Available from: http://hdl.handle.net/2123/20115

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

10. Joshi, Shalmali Dilip. Constraint based approaches to interpretable and semi-supervised machine learning.

Degree: PhD, Electrical and Computer Engineering, 2019, University of Texas – Austin

 Interpretability and Explainability of machine learning algorithms are becoming increasingly important as Machine Learning (ML) systems get widely applied to domains like clinical healthcare, social… (more)

Subjects/Keywords: Interpretable machine learning; Semi-supervised machine learning

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APA (6th Edition):

Joshi, S. D. (2019). Constraint based approaches to interpretable and semi-supervised machine learning. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/1259

Chicago Manual of Style (16th Edition):

Joshi, Shalmali Dilip. “Constraint based approaches to interpretable and semi-supervised machine learning.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed November 20, 2019. http://dx.doi.org/10.26153/tsw/1259.

MLA Handbook (7th Edition):

Joshi, Shalmali Dilip. “Constraint based approaches to interpretable and semi-supervised machine learning.” 2019. Web. 20 Nov 2019.

Vancouver:

Joshi SD. Constraint based approaches to interpretable and semi-supervised machine learning. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2019 Nov 20]. Available from: http://dx.doi.org/10.26153/tsw/1259.

Council of Science Editors:

Joshi SD. Constraint based approaches to interpretable and semi-supervised machine learning. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/1259


University of Western Ontario

11. Ao, Shuang. Visual Transfer Learning in the Absence of the Source Data.

Degree: 2017, University of Western Ontario

 Image recognition has become one of the most popular topics in machine learning. With the development of Deep Convolutional Neural Networks (CNN) and the help… (more)

Subjects/Keywords: Visual Transfer Learning; Hypothesis Transfer Learning; Supervised Learning; Semi-supervised Learning; Artificial Intelligence and Robotics

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APA (6th Edition):

Ao, S. (2017). Visual Transfer Learning in the Absence of the Source Data. (Thesis). University of Western Ontario. Retrieved from https://ir.lib.uwo.ca/etd/4463

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ao, Shuang. “Visual Transfer Learning in the Absence of the Source Data.” 2017. Thesis, University of Western Ontario. Accessed November 20, 2019. https://ir.lib.uwo.ca/etd/4463.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ao, Shuang. “Visual Transfer Learning in the Absence of the Source Data.” 2017. Web. 20 Nov 2019.

Vancouver:

Ao S. Visual Transfer Learning in the Absence of the Source Data. [Internet] [Thesis]. University of Western Ontario; 2017. [cited 2019 Nov 20]. Available from: https://ir.lib.uwo.ca/etd/4463.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ao S. Visual Transfer Learning in the Absence of the Source Data. [Thesis]. University of Western Ontario; 2017. Available from: https://ir.lib.uwo.ca/etd/4463

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

12. Byun, Byungki. On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling.

Degree: PhD, Electrical and Computer Engineering, 2012, Georgia Tech

 This dissertation presents the development of a semi-supervised incremental learning framework with a multi-view perspective for image concept modeling. For reliable image concept characterization, having… (more)

Subjects/Keywords: Discriminative learning; Semi-supervised learning; Incremental learning; Image modeling; Multi-view learning; Machine learning; Supervised learning (Machine learning); Boosting (Algorithms)

Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7

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APA (6th Edition):

Byun, B. (2012). On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/43597

Chicago Manual of Style (16th Edition):

Byun, Byungki. “On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling.” 2012. Doctoral Dissertation, Georgia Tech. Accessed November 20, 2019. http://hdl.handle.net/1853/43597.

MLA Handbook (7th Edition):

Byun, Byungki. “On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling.” 2012. Web. 20 Nov 2019.

Vancouver:

Byun B. On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Nov 20]. Available from: http://hdl.handle.net/1853/43597.

Council of Science Editors:

Byun B. On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/43597


University of Alberta

13. Mahmood, Ashique. Automatic step-size adaptation in incremental supervised learning.

Degree: MS, Department of Computing Science, 2010, University of Alberta

 Performance and stability of many iterative algorithms such as stochastic gradient descent largely depend on a fixed and scalar step-size parameter. Use of a fixed… (more)

Subjects/Keywords: step size; supervised learning; stochastic gradient descent

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APA (6th Edition):

Mahmood, A. (2010). Automatic step-size adaptation in incremental supervised learning. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/zc77sr03r

Chicago Manual of Style (16th Edition):

Mahmood, Ashique. “Automatic step-size adaptation in incremental supervised learning.” 2010. Masters Thesis, University of Alberta. Accessed November 20, 2019. https://era.library.ualberta.ca/files/zc77sr03r.

MLA Handbook (7th Edition):

Mahmood, Ashique. “Automatic step-size adaptation in incremental supervised learning.” 2010. Web. 20 Nov 2019.

Vancouver:

Mahmood A. Automatic step-size adaptation in incremental supervised learning. [Internet] [Masters thesis]. University of Alberta; 2010. [cited 2019 Nov 20]. Available from: https://era.library.ualberta.ca/files/zc77sr03r.

Council of Science Editors:

Mahmood A. Automatic step-size adaptation in incremental supervised learning. [Masters Thesis]. University of Alberta; 2010. Available from: https://era.library.ualberta.ca/files/zc77sr03r


Baylor University

14. [No author]. Semi-supervised learning for electrocardiography signal classification.

Degree: 2018, Baylor University

 An electrocardiogram (ECG) is a cardiology test that provides information about the structure and function of the heart. The size of the ECG data collected… (more)

Subjects/Keywords: Semi-supervised learning; Electrocardiography; pattern recognition

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APA (6th Edition):

author], [. (2018). Semi-supervised learning for electrocardiography signal classification. (Thesis). Baylor University. Retrieved from http://hdl.handle.net/2104/10391

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

author], [No. “Semi-supervised learning for electrocardiography signal classification. ” 2018. Thesis, Baylor University. Accessed November 20, 2019. http://hdl.handle.net/2104/10391.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

author], [No. “Semi-supervised learning for electrocardiography signal classification. ” 2018. Web. 20 Nov 2019.

Vancouver:

author] [. Semi-supervised learning for electrocardiography signal classification. [Internet] [Thesis]. Baylor University; 2018. [cited 2019 Nov 20]. Available from: http://hdl.handle.net/2104/10391.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

author] [. Semi-supervised learning for electrocardiography signal classification. [Thesis]. Baylor University; 2018. Available from: http://hdl.handle.net/2104/10391

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Manchester

15. Rostamniakankalhori, Sharareh. Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course.

Degree: 2011, University of Manchester

 Tuberculosis (TB) is an infectious disease which is a global public health problem with over 9 million new cases annually. Tuberculosis treatment, with patient supervision… (more)

Subjects/Keywords: Integrated Supervised and Unsupervised Learning; Tuberculosis; plediction

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APA (6th Edition):

Rostamniakankalhori, S. (2011). Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404

Chicago Manual of Style (16th Edition):

Rostamniakankalhori, Sharareh. “Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course.” 2011. Doctoral Dissertation, University of Manchester. Accessed November 20, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404.

MLA Handbook (7th Edition):

Rostamniakankalhori, Sharareh. “Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course.” 2011. Web. 20 Nov 2019.

Vancouver:

Rostamniakankalhori S. Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course. [Internet] [Doctoral dissertation]. University of Manchester; 2011. [cited 2019 Nov 20]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404.

Council of Science Editors:

Rostamniakankalhori S. Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course. [Doctoral Dissertation]. University of Manchester; 2011. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404


McMaster University

16. Ateeq, Sameen. Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries.

Degree: MSc, 2018, McMaster University

According to the Public Health Agency of Canada, falls account for 95% of all hip fractures in Canada; 20% of fall-related injury cases end in… (more)

Subjects/Keywords: machine learning; supervised classification; falls; CCHS; injuries

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APA (6th Edition):

Ateeq, S. (2018). Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/24095

Chicago Manual of Style (16th Edition):

Ateeq, Sameen. “Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries.” 2018. Masters Thesis, McMaster University. Accessed November 20, 2019. http://hdl.handle.net/11375/24095.

MLA Handbook (7th Edition):

Ateeq, Sameen. “Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries.” 2018. Web. 20 Nov 2019.

Vancouver:

Ateeq S. Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries. [Internet] [Masters thesis]. McMaster University; 2018. [cited 2019 Nov 20]. Available from: http://hdl.handle.net/11375/24095.

Council of Science Editors:

Ateeq S. Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries. [Masters Thesis]. McMaster University; 2018. Available from: http://hdl.handle.net/11375/24095


Halmstad University

17. Xiang, Gao. Supervised Methods for Fault Detection in Vehicle.

Degree: Intelligent systems (IS-lab), 2010, Halmstad University

  Uptime and maintenance planning are important issues for vehicle operators (e.g.operators of bus fleets). Unplanned downtime can cause a bus operator to be fined… (more)

Subjects/Keywords: fault detection; Supervised Methods; Machine Learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Xiang, G. (2010). Supervised Methods for Fault Detection in Vehicle. (Thesis). Halmstad University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-14151

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Xiang, Gao. “Supervised Methods for Fault Detection in Vehicle.” 2010. Thesis, Halmstad University. Accessed November 20, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-14151.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Xiang, Gao. “Supervised Methods for Fault Detection in Vehicle.” 2010. Web. 20 Nov 2019.

Vancouver:

Xiang G. Supervised Methods for Fault Detection in Vehicle. [Internet] [Thesis]. Halmstad University; 2010. [cited 2019 Nov 20]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-14151.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Xiang G. Supervised Methods for Fault Detection in Vehicle. [Thesis]. Halmstad University; 2010. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-14151

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

18. Zhao, Xuran. Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics.

Degree: Docteur es, Signal et images, 2013, Paris, ENST

Dans la plupart des systèmes biométriques de l’état de l’art, les données biométrique sont souvent représentés par des vecteurs de grande dimensionalité. La dimensionnalité d'éléments… (more)

Subjects/Keywords: Apprentissage semi-supervisé; Semi-supervised learning

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APA (6th Edition):

Zhao, X. (2013). Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics. (Doctoral Dissertation). Paris, ENST. Retrieved from http://www.theses.fr/2013ENST0061

Chicago Manual of Style (16th Edition):

Zhao, Xuran. “Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics.” 2013. Doctoral Dissertation, Paris, ENST. Accessed November 20, 2019. http://www.theses.fr/2013ENST0061.

MLA Handbook (7th Edition):

Zhao, Xuran. “Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics.” 2013. Web. 20 Nov 2019.

Vancouver:

Zhao X. Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics. [Internet] [Doctoral dissertation]. Paris, ENST; 2013. [cited 2019 Nov 20]. Available from: http://www.theses.fr/2013ENST0061.

Council of Science Editors:

Zhao X. Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics. [Doctoral Dissertation]. Paris, ENST; 2013. Available from: http://www.theses.fr/2013ENST0061


Georgia Tech

19. Ahsan, Unaiza. Leveraging Mid-level Representations for Complex Activity Recognition.

Degree: PhD, Interactive Computing, 2019, Georgia Tech

 Dynamic scene understanding requires learning representations of the components of the scene including objects, environments, actions and events. Complex activity recognition from images and videos… (more)

Subjects/Keywords: activity recognition; self-supervised learning; event recognition

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APA (6th Edition):

Ahsan, U. (2019). Leveraging Mid-level Representations for Complex Activity Recognition. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61199

Chicago Manual of Style (16th Edition):

Ahsan, Unaiza. “Leveraging Mid-level Representations for Complex Activity Recognition.” 2019. Doctoral Dissertation, Georgia Tech. Accessed November 20, 2019. http://hdl.handle.net/1853/61199.

MLA Handbook (7th Edition):

Ahsan, Unaiza. “Leveraging Mid-level Representations for Complex Activity Recognition.” 2019. Web. 20 Nov 2019.

Vancouver:

Ahsan U. Leveraging Mid-level Representations for Complex Activity Recognition. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2019 Nov 20]. Available from: http://hdl.handle.net/1853/61199.

Council of Science Editors:

Ahsan U. Leveraging Mid-level Representations for Complex Activity Recognition. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61199


University of Notre Dame

20. Troy William Raeder. Evaluating and Maintaining Classification Algorithms</h1>.

Degree: PhD, Computer Science and Engineering, 2012, University of Notre Dame

  Any practical application of machine learning necessarily begins with the selection of a classification algorithm. Generally, practitioners will try several different types of algorithms… (more)

Subjects/Keywords: classification; supervised learning; evaluation; concept drift

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Raeder, T. W. (2012). Evaluating and Maintaining Classification Algorithms</h1>. (Doctoral Dissertation). University of Notre Dame. Retrieved from https://curate.nd.edu/show/4b29b56616h

Chicago Manual of Style (16th Edition):

Raeder, Troy William. “Evaluating and Maintaining Classification Algorithms</h1>.” 2012. Doctoral Dissertation, University of Notre Dame. Accessed November 20, 2019. https://curate.nd.edu/show/4b29b56616h.

MLA Handbook (7th Edition):

Raeder, Troy William. “Evaluating and Maintaining Classification Algorithms</h1>.” 2012. Web. 20 Nov 2019.

Vancouver:

Raeder TW. Evaluating and Maintaining Classification Algorithms</h1>. [Internet] [Doctoral dissertation]. University of Notre Dame; 2012. [cited 2019 Nov 20]. Available from: https://curate.nd.edu/show/4b29b56616h.

Council of Science Editors:

Raeder TW. Evaluating and Maintaining Classification Algorithms</h1>. [Doctoral Dissertation]. University of Notre Dame; 2012. Available from: https://curate.nd.edu/show/4b29b56616h


University of North Texas

21. Dandala, Bharath. Multilingual Word Sense Disambiguation Using Wikipedia.

Degree: 2013, University of North Texas

 Ambiguity is inherent to human language. In particular, word sense ambiguity is prevalent in all natural languages, with a large number of the words in… (more)

Subjects/Keywords: Wikipedia; word sense disambiguation; supervised learning; multilingual

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Dandala, B. (2013). Multilingual Word Sense Disambiguation Using Wikipedia. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc500036/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Dandala, Bharath. “Multilingual Word Sense Disambiguation Using Wikipedia.” 2013. Thesis, University of North Texas. Accessed November 20, 2019. https://digital.library.unt.edu/ark:/67531/metadc500036/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Dandala, Bharath. “Multilingual Word Sense Disambiguation Using Wikipedia.” 2013. Web. 20 Nov 2019.

Vancouver:

Dandala B. Multilingual Word Sense Disambiguation Using Wikipedia. [Internet] [Thesis]. University of North Texas; 2013. [cited 2019 Nov 20]. Available from: https://digital.library.unt.edu/ark:/67531/metadc500036/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Dandala B. Multilingual Word Sense Disambiguation Using Wikipedia. [Thesis]. University of North Texas; 2013. Available from: https://digital.library.unt.edu/ark:/67531/metadc500036/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


King Abdullah University of Science and Technology

22. Xu, Mengmeng. Object Detection Using Multiple Level Annotations.

Degree: 2019, King Abdullah University of Science and Technology

 Object detection is a fundamental problem in computer vision. Impressive results have been achieved on large-scale detection benchmarks by fully-supervised object detection (FSOD) methods. However,… (more)

Subjects/Keywords: Object Detection; Hybrid Supervised Learning; Training Budget

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Xu, M. (2019). Object Detection Using Multiple Level Annotations. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/631958

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Xu, Mengmeng. “Object Detection Using Multiple Level Annotations.” 2019. Thesis, King Abdullah University of Science and Technology. Accessed November 20, 2019. http://hdl.handle.net/10754/631958.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Xu, Mengmeng. “Object Detection Using Multiple Level Annotations.” 2019. Web. 20 Nov 2019.

Vancouver:

Xu M. Object Detection Using Multiple Level Annotations. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2019. [cited 2019 Nov 20]. Available from: http://hdl.handle.net/10754/631958.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Xu M. Object Detection Using Multiple Level Annotations. [Thesis]. King Abdullah University of Science and Technology; 2019. Available from: http://hdl.handle.net/10754/631958

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Linköping University

23. Alirezaie, Marjan. Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation.

Degree: Computer and Information Science, 2011, Linköping University

  The present thesis addresses machine learning in a domain of naturallanguage phrases that are names of universities. It describes two approaches to this problem… (more)

Subjects/Keywords: Machine Learning; Supervised Learning; Unsupervised Learning; Computer Sciences; Datavetenskap (datalogi)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Alirezaie, M. (2011). Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Alirezaie, Marjan. “Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation.” 2011. Thesis, Linköping University. Accessed November 20, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Alirezaie, Marjan. “Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation.” 2011. Web. 20 Nov 2019.

Vancouver:

Alirezaie M. Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. [Internet] [Thesis]. Linköping University; 2011. [cited 2019 Nov 20]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Alirezaie M. Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. [Thesis]. Linköping University; 2011. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Oregon State University

24. Hao, Guohua. Efficient training and feature induction in sequential supervised learning.

Degree: PhD, Computer Science, 2009, Oregon State University

 Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature… (more)

Subjects/Keywords: Machine Learning; Supervised learning (Machine learning)  – Mathematical models

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hao, G. (2009). Efficient training and feature induction in sequential supervised learning. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/12548

Chicago Manual of Style (16th Edition):

Hao, Guohua. “Efficient training and feature induction in sequential supervised learning.” 2009. Doctoral Dissertation, Oregon State University. Accessed November 20, 2019. http://hdl.handle.net/1957/12548.

MLA Handbook (7th Edition):

Hao, Guohua. “Efficient training and feature induction in sequential supervised learning.” 2009. Web. 20 Nov 2019.

Vancouver:

Hao G. Efficient training and feature induction in sequential supervised learning. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2019 Nov 20]. Available from: http://hdl.handle.net/1957/12548.

Council of Science Editors:

Hao G. Efficient training and feature induction in sequential supervised learning. [Doctoral Dissertation]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/12548


University of New South Wales

25. Wang, Weihong. A Weakly Supervised Approach for Object Detection.

Degree: Computer Science & Engineering, 2016, University of New South Wales

 Object detection in images and videos is an important topic in computer vision. In general, a large number of training samples are required to train… (more)

Subjects/Keywords: Boosting; Weakly supervised learning; Multiple instance learning; Object detection; Online learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wang, W. (2016). A Weakly Supervised Approach for Object Detection. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/56619 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41053/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Wang, Weihong. “A Weakly Supervised Approach for Object Detection.” 2016. Doctoral Dissertation, University of New South Wales. Accessed November 20, 2019. http://handle.unsw.edu.au/1959.4/56619 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41053/SOURCE02?view=true.

MLA Handbook (7th Edition):

Wang, Weihong. “A Weakly Supervised Approach for Object Detection.” 2016. Web. 20 Nov 2019.

Vancouver:

Wang W. A Weakly Supervised Approach for Object Detection. [Internet] [Doctoral dissertation]. University of New South Wales; 2016. [cited 2019 Nov 20]. Available from: http://handle.unsw.edu.au/1959.4/56619 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41053/SOURCE02?view=true.

Council of Science Editors:

Wang W. A Weakly Supervised Approach for Object Detection. [Doctoral Dissertation]. University of New South Wales; 2016. Available from: http://handle.unsw.edu.au/1959.4/56619 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41053/SOURCE02?view=true


Georgia Tech

26. Berlind, Christopher. New insights on the power of active learning.

Degree: PhD, Computer Science, 2015, Georgia Tech

 Traditional supervised machine learning algorithms are expected to have access to a large corpus of labeled examples, but the massive amount of data available in… (more)

Subjects/Keywords: Machine learning; Learning theory; Active learning; Semi-supervised learning; Domain adaptation; Large margin learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Berlind, C. (2015). New insights on the power of active learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53948

Chicago Manual of Style (16th Edition):

Berlind, Christopher. “New insights on the power of active learning.” 2015. Doctoral Dissertation, Georgia Tech. Accessed November 20, 2019. http://hdl.handle.net/1853/53948.

MLA Handbook (7th Edition):

Berlind, Christopher. “New insights on the power of active learning.” 2015. Web. 20 Nov 2019.

Vancouver:

Berlind C. New insights on the power of active learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Nov 20]. Available from: http://hdl.handle.net/1853/53948.

Council of Science Editors:

Berlind C. New insights on the power of active learning. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53948


Carnegie Mellon University

27. Zhang, Yi. Learning with Limited Supervision by Input and Output Coding.

Degree: 2012, Carnegie Mellon University

 In many real-world applications of supervised learning, only a limited number of labeled examples are available because the cost of obtaining high-quality examples is high.… (more)

Subjects/Keywords: regularization; error-correcting output codes; supervised learning; semi-supervised learning; multi-task learning; multi-label classification; dimensionality reduction; Computer Sciences

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APA (6th Edition):

Zhang, Y. (2012). Learning with Limited Supervision by Input and Output Coding. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/156

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Zhang, Yi. “Learning with Limited Supervision by Input and Output Coding.” 2012. Thesis, Carnegie Mellon University. Accessed November 20, 2019. http://repository.cmu.edu/dissertations/156.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zhang, Yi. “Learning with Limited Supervision by Input and Output Coding.” 2012. Web. 20 Nov 2019.

Vancouver:

Zhang Y. Learning with Limited Supervision by Input and Output Coding. [Internet] [Thesis]. Carnegie Mellon University; 2012. [cited 2019 Nov 20]. Available from: http://repository.cmu.edu/dissertations/156.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhang Y. Learning with Limited Supervision by Input and Output Coding. [Thesis]. Carnegie Mellon University; 2012. Available from: http://repository.cmu.edu/dissertations/156

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Georgia

28. Mahamuda, Vasim. Analyzing the performance of machine learning algorithms on metagenomic data.

Degree: MS, Computer Science, 2010, University of Georgia

 Metagenomics is a branch of bioinformatics that deals with the study and analysis of micro-organisms in natural environments. Some micro-organisms including many species of bacteria,… (more)

Subjects/Keywords: Binning; decision trees; machine learning; metagenomics; ensemble methods; supervised learning.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Mahamuda, V. (2010). Analyzing the performance of machine learning algorithms on metagenomic data. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/mahamuda_vasim_201008_ms

Chicago Manual of Style (16th Edition):

Mahamuda, Vasim. “Analyzing the performance of machine learning algorithms on metagenomic data.” 2010. Masters Thesis, University of Georgia. Accessed November 20, 2019. http://purl.galileo.usg.edu/uga_etd/mahamuda_vasim_201008_ms.

MLA Handbook (7th Edition):

Mahamuda, Vasim. “Analyzing the performance of machine learning algorithms on metagenomic data.” 2010. Web. 20 Nov 2019.

Vancouver:

Mahamuda V. Analyzing the performance of machine learning algorithms on metagenomic data. [Internet] [Masters thesis]. University of Georgia; 2010. [cited 2019 Nov 20]. Available from: http://purl.galileo.usg.edu/uga_etd/mahamuda_vasim_201008_ms.

Council of Science Editors:

Mahamuda V. Analyzing the performance of machine learning algorithms on metagenomic data. [Masters Thesis]. University of Georgia; 2010. Available from: http://purl.galileo.usg.edu/uga_etd/mahamuda_vasim_201008_ms


The Ohio State University

29. Sinha, Kaushik. New Directions in Gaussian Mixture Learning and Semi-supervised Learning.

Degree: PhD, Computer Science and Engineering, 2010, The Ohio State University

  High dimensional data analysis involves, among many other tasks, modeling the unknown underlying data generating process and predicting one of the few possible sources… (more)

Subjects/Keywords: Computer Science; Gaussian Mixture Learning; Semi-supervised Learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sinha, K. (2010). New Directions in Gaussian Mixture Learning and Semi-supervised Learning. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1284062001

Chicago Manual of Style (16th Edition):

Sinha, Kaushik. “New Directions in Gaussian Mixture Learning and Semi-supervised Learning.” 2010. Doctoral Dissertation, The Ohio State University. Accessed November 20, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1284062001.

MLA Handbook (7th Edition):

Sinha, Kaushik. “New Directions in Gaussian Mixture Learning and Semi-supervised Learning.” 2010. Web. 20 Nov 2019.

Vancouver:

Sinha K. New Directions in Gaussian Mixture Learning and Semi-supervised Learning. [Internet] [Doctoral dissertation]. The Ohio State University; 2010. [cited 2019 Nov 20]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1284062001.

Council of Science Editors:

Sinha K. New Directions in Gaussian Mixture Learning and Semi-supervised Learning. [Doctoral Dissertation]. The Ohio State University; 2010. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1284062001


Oregon State University

30. Ashenfelter, Adam J. Sequential supervised learning and conditional random fields.

Degree: MS, Computer Science, 2003, Oregon State University

Supervised learning is concerned with discovering the relationship between example sets of features and their corresponding classes. The traditional supervised learning formulation assumes that all… (more)

Subjects/Keywords: Supervised learning (Machine learning)

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APA (6th Edition):

Ashenfelter, A. J. (2003). Sequential supervised learning and conditional random fields. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/10165

Chicago Manual of Style (16th Edition):

Ashenfelter, Adam J. “Sequential supervised learning and conditional random fields.” 2003. Masters Thesis, Oregon State University. Accessed November 20, 2019. http://hdl.handle.net/1957/10165.

MLA Handbook (7th Edition):

Ashenfelter, Adam J. “Sequential supervised learning and conditional random fields.” 2003. Web. 20 Nov 2019.

Vancouver:

Ashenfelter AJ. Sequential supervised learning and conditional random fields. [Internet] [Masters thesis]. Oregon State University; 2003. [cited 2019 Nov 20]. Available from: http://hdl.handle.net/1957/10165.

Council of Science Editors:

Ashenfelter AJ. Sequential supervised learning and conditional random fields. [Masters Thesis]. Oregon State University; 2003. Available from: http://hdl.handle.net/1957/10165

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